M2-type TAMs are increasingly implicated as a crucial factor promoting metastasis. Numerous cell types dictate monocyte differentiation into M2 TAMs via a complex network of cytokine-based communication. Elucidating critical pathways in this network can provide new targets for inhibiting metastasis. In this study, we focused on cancer cells, CAFs, and monocytes as a major node in this network. Monocyte cocultures with cancer-stimulated CAFs were used to investigate differentiation into M2-like TAMs. Cytokine array analyses were employed to discover the CAF-derived regulators of differentiation. These regulators were validated in primary CAFs and bone marrow-derived monocytes. Orthotopic, syngeneic colon carcinoma models using cotransplanted CAFs were established to observe effects on tumor growth and metastasis. To confirm a correlation with clinical evidence, meta-analyses were employed using the Oncomine database. Our coculture studies identify IL6 and GM-CSF as the pivotal signals released from cancer cell-activated CAFs that cooperate to induce monocyte differentiation into M2-like TAMs. In orthotopic, syngeneic colon carcinoma mouse models, cotransplanted CAFs elevated IL6 and GM-CSF levels, TAM infiltration, and metastasis. These pathologic effects were dramatically reversed by joint IL6 and GM-CSF blockade. A positive correlation between GM-CSF and IL6 expression and disease course was observed by meta-analyses of the clinical data. Our studies indicate a significant reappraisal of the role of IL6 and GM-CSF in metastasis and implicate CAFs as the "henchmen" for cancer cells in producing an immunosuppressive tumor ecological niche. Dual targeting of GM-CSF and IL6 is a promising new approach for inhibiting metastasis. .
Background Predicting the drug response of a patient is important for precision oncology. In recent studies, multi-omics data have been used to improve the prediction accuracy of drug response. Although multi-omics data are good resources for drug response prediction, the large dimension of data tends to hinder performance improvement. In this study, we aimed to develop a new method, which can effectively reduce the large dimension of data, based on the supervised deep learning model for predicting drug response. Results We proposed a novel method called Supervised Feature Extraction Learning using Triplet loss (Super.FELT) for drug response prediction. Super.FELT consists of three stages, namely, feature selection, feature encoding using a supervised method, and binary classification of drug response (sensitive or resistant). We used multi-omics data including mutation, copy number aberration, and gene expression, and these were obtained from cell lines [Genomics of Drug Sensitivity in Cancer (GDSC), Cancer Cell Line Encyclopedia (CCLE), and Cancer Therapeutics Response Portal (CTRP)], patient-derived tumor xenografts (PDX), and The Cancer Genome Atlas (TCGA). GDSC was used for training and cross-validation tests, and CCLE, CTRP, PDX, and TCGA were used for external validation. We performed ablation studies for the three stages and verified that the use of multi-omics data guarantees better performance of drug response prediction. Our results verified that Super.FELT outperformed the other methods at external validation on PDX and TCGA and was good at cross-validation on GDSC and external validation on CCLE and CTRP. In addition, through our experiments, we confirmed that using multi-omics data is useful for external non-cell line data. Conclusion By separating the three stages, Super.FELT achieved better performance than the other methods. Through our results, we found that it is important to train encoders and a classifier independently, especially for external test on PDX and TCGA. Moreover, although gene expression is the most powerful data on cell line data, multi-omics promises better performance for external validation on non-cell line data than gene expression data. Source codes of Super.FELT are available at https://github.com/DMCB-GIST/Super.FELT.
Identification of drug-target interactions (DTIs) plays a crucial role in drug development. Traditional laboratory-based DTI discovery is generally costly and time-consuming. Therefore, computational approaches have been developed to predict interactions between drug candidates and disease-causing proteins. We designed a novel method, termed heterogeneous information integration for DTI prediction (HIDTI), based on the concept of predicting vectors for all of unknown/unavailable heterogeneous drug- and protein-related information. We applied a residual network in HIDTI to extract features of such heterogeneous information for predicting DTIs, and tested the model using drug-based ten-fold cross-validation to examine the prediction performance for unseen drugs. As a result, HIDTI outperformed existing models using heterogeneous information, and was demonstrating that our method predicted heterogeneous information on unseen data better than other models. In conclusion, our study suggests that HIDTI has the potential to advance the field of drug development by accurately predicting the targets of new drugs.
MicroRNAs (miRNAs) are key molecules that regulate biological processes such as cell proliferation, differentiation, and apoptosis in cancer. Somatic copy number alterations (SCNAs) are common genetic mutations that play essential roles in cancer development. Here, we investigated the association between miRNAs and SCNAs in cancer. We collected 2538 tumor samples for seven cancer types from The Cancer Genome Atlas. We found that 32−84% of miRNAs are in SCNA regions, with the rate depending on the cancer type. In these regions, we identified 80 SCNA-miRNAs whose expression was mainly associated with SCNAs in at least one cancer type and showed that these SCNA-miRNAs are related to cancer by survival analysis and literature searching. We also identified 58 SCNA-miRNAs common in the seven cancer types (CC-SCNA-miRNAs) and showed that these CC-SCNA-miRNAs are more likely to be related with protein and gene expression than other miRNAs. Furthermore, we experimentally validated the oncogenic role of miR-589. In conclusion, our results suggest that SCNA-miRNAs significantly alter biological processes related to cancer development, confirming the importance of SCNAs in non-coding regions in cancer.
<div>Abstract<p><b>Purpose:</b> M2-type TAMs are increasingly implicated as a crucial factor promoting metastasis. Numerous cell types dictate monocyte differentiation into M2 TAMs via a complex network of cytokine-based communication. Elucidating critical pathways in this network can provide new targets for inhibiting metastasis. In this study, we focused on cancer cells, CAFs, and monocytes as a major node in this network.</p><p><b>Experimental Design:</b> Monocyte cocultures with cancer-stimulated CAFs were used to investigate differentiation into M2-like TAMs. Cytokine array analyses were employed to discover the CAF-derived regulators of differentiation. These regulators were validated in primary CAFs and bone marrow-derived monocytes. Orthotopic, syngeneic colon carcinoma models using cotransplanted CAFs were established to observe effects on tumor growth and metastasis. To confirm a correlation with clinical evidence, meta-analyses were employed using the Oncomine database.</p><p><b>Results:</b> Our coculture studies identify IL6 and GM-CSF as the pivotal signals released from cancer cell–activated CAFs that cooperate to induce monocyte differentiation into M2-like TAMs. In orthotopic, syngeneic colon carcinoma mouse models, cotransplanted CAFs elevated IL6 and GM-CSF levels, TAM infiltration, and metastasis. These pathologic effects were dramatically reversed by joint IL6 and GM-CSF blockade. A positive correlation between GM-CSF and IL6 expression and disease course was observed by meta-analyses of the clinical data.</p><p><b>Conclusions:</b> Our studies indicate a significant reappraisal of the role of IL6 and GM-CSF in metastasis and implicate CAFs as the “henchmen” for cancer cells in producing an immunosuppressive tumor ecological niche. Dual targeting of GM-CSF and IL6 is a promising new approach for inhibiting metastasis. <i>Clin Cancer Res; 24(21); 5407–21. ©2018 AACR</i>.</p></div>
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